Data sampling is a fundamental statistical method used in various fields to extract meaningful insights from large datasets. By analyzing a subset of data, researchers can draw conclusions about the entire population with accuracy and efficiency.
This article will explore the concept of data sampling, its importance, techniques, process, advantages, disadvantages, and best practices for effective implementation.
Table of Content
What is Data Sampling?
Data Sampling is a statistical method that is used to analyze and observe a subset of data from a larger piece of dataset and configure meaningful information, all the required info from the subset that helps in gaining information, or drawing conclusion for the larger dataset, or it’s parent dataset.
- Sampling in data science helps in finding more better and accurate results and works best when the data size is big.
- Sampling helps in identifying the entire pattern on which the subset of the dataset is based upon and on the basis of that smaller dataset, entire sample size is presumed to hold the same properties.
- It is a quicker and more effective method to draw conclusions.
What is Data Sampling important?
Data sampling is important for a couple of key reasons:
- Cost and Time Efficiency: Sampling allows researchers to collect and analyze a subset of data rather than the entire population. This reduces the time and resources required for data collection and analysis, making it more cost-effective, especially when dealing with large datasets.
- Feasibility: In many cases, it’s impractical or impossible to analyze the entire population due to constraints such as time, budget, or accessibility. Sampling makes it feasible to study a representative portion of the population while still yielding reliable results.
- Risk Reduction: Sampling helps mitigate the risk of errors or biases that may occur when analyzing the entire population. By selecting a random or systematic sample, researchers can minimize the impact of outliers or anomalies that could skew the results.
- Accuracy: In some cases, examining the entire population might not even be possible. For instance, testing every single item in a large batch of manufactured goods would be impractical. Data sampling allows researchers to get a good understanding of the whole population by examining a well-chosen subset.
Types of Data Sampling Techniques
There are mainly two types of Data Sampling techniques which are further divided into 4 sub-categories each. They are as follows:
Probability Data Sampling Technique
Probability Data Sampling technique involves selecting data points from a dataset in such a way that every data point has an equal chance of being chosen. Probability sampling techniques ensure that the sample is representative of the population from which it is drawn, making it possible to generalize the findings from the sample to the entire population with a known level of confidence.
- Simple Random Sampling: In Simple random sampling, every dataset has an equal chance or probability of being selected. For eg. Selection of head or tail. Both of the outcomes of the event have equal probabilities of getting selected.
- Systematic Sampling: In Systematic sampling, a regular interval is chosen each after which the dataset continues for sampling. It is more easier and regular than the previous method of sampling and reduces inefficiency while improving the speed. For eg. In a series of 10 numbers, we have a sampling after every 2nd number. Here we use the process of Systematic sampling.
- Stratified Sampling: In Stratified sampling, we follow the strategy of divide & conquer. We opt for the strategy of dividing into groups on the basis of similar properties and then perform sampling. This ensures better accuracy. For eg. In a workplace data, the total number of employees is divided among men and women.
- Cluster Sampling: Cluster sampling is more or less like stratified sampling. However in cluster sampling we choose random data and form it in groups, whereas in stratified we use strata, or an orderly division takes place in the latter. For eg. Picking up users of different networks from a total combination of users.
Non-Probability Data Sampling
Non-probability data sampling means that the selection happens on a non-random basis, and it depends on the individual as to which data does it want to pick. There is no random selection and every selection is made by a thought and an idea behind it.
- Convenience Sampling: As the name suggests, the data checker selects the data based on his/her convenience. It may choose the data sets that would require lesser calculations, and save time while bringing results at par with probability data sampling technique. For eg. Dataset involving recruitment of people in IT Industry, where the convenience would be to choose the data which is the latest one, and the one which encompasses youngsters more.
- Voluntary Response Sampling: As the name suggests, this sampling method depends on the voluntary response of the audience for the data. For eg. If a survey is being conducted on types of Blood groups found in majority at a particular place, and the people who are willing to take part in this survey, and then if the data sampling is conducted, it will be referred to as the voluntary response sampling.
- Purposive Sampling: The Sampling method that involves a special purpose falls under purposive sampling. For eg. If we need to tackle the need of education, we may conduct a survey in the rural areas and then create a dataset based on people’s responses. Such type of sampling is called Purposive Sampling.
- Snowball Sampling: Snowball sampling technique takes place via contacts. For eg. If we wish to conduct a survey on the people living in slum areas, and one person contacts us to the other and so on, it is called a process of snowball sampling.
Data Sampling Process
The process of data sampling involves the following steps:
- Find a Target Dataset: Identify the dataset that you want to analyze or draw conclusions about. This dataset represents the larger population from which a sample will be drawn.
- Select a Sample Size: Determine the size of the sample you will collect from the target dataset. The sample size is the subset of the larger dataset on which the sampling process will be performed.
- Decide the Sampling Technique: Choose a suitable sampling technique from options such as Simple Random Sampling, Systematic Sampling, Cluster Sampling, Snowball Sampling, or Stratified Sampling. The choice of technique depends on factors such as the nature of the dataset and the research objectives.
- Perform Sampling: Apply the selected sampling technique to collect data from the target dataset. Ensure that the sampling process is carried out systematically and according to the chosen method.
- Draw Inferences for the Entire Dataset: Analyze the properties and characteristics of the sampled data subset. Use statistical methods and analysis techniques to draw inferences and insights that are representative of the entire dataset.
- Extend Properties to the Entire Dataset: Extend the findings and conclusions derived from the sample to the entire target dataset. This involves extrapolating the insights gained from the sample to make broader statements or predictions about the larger population.
Advantages of Data Sampling
- Data Sampling helps draw conclusions, or inferences of larger datasets using a smaller sample space, which concerns the entire dataset.
- It helps save time and is a quicker and faster approach.
- It is a better way in terms of cost effectiveness as it reduces the cost for data analysis, observation and collection. It is more of like gaining the data, applying sampling method & drawing the conclusion.
- It is more accurate in terms of result and conclusion.
Disadvantages of Data Sampling
- Sampling Error: It is the act of differentiation among the entire sample size and the smaller dataset. There arise some differences in characteristics, or properties among both the datasets that reduce the accuracy and the sample set is unable to represent a larger piece of information. Sampling Error mostly occurs by a chance and is regarded as an error-less term.
- It becomes difficult in a few data sampling methods, such as forming clusters of similar properties.
- Sampling Bias: It is the process of choosing a sample set which does not represent the entire population on a whole. It occurs mostly due to incorrect method of sampling usage and consists of errors as the given dataset is not properly able to draw conclusions for the larger set of data.
Sample Size Determination
Sample size is the universal dataset concerning to which several other smaller datasets are created that help in inferring the properties of the entire dataset. Following are a series of steps that are involved during sample size determination.
- Firstly calculate the population size, as in the total sample space size on which the sampling has to be performed.
- Find the values of confidence levels that represent the accuracy of the data.
- Find the value of error margins if any with respect to the sample space dataset.
- Calculate the deviation from the mean or average value from that of standard deviation value calculated.
Best Practices for Effective Data Sampling
Before performing data sampling methods, one should keep in mind the below three mentioned considerations for effective data sampling.
- Statistical Regularity: A larger sample space, or parent dataset means more accurate results. This is because then the probability of every data to be chosen is equal, ie., regular. When picked at random, a larger data ensures a regularity among all the data.
- Dataset must be accurate and verified from the respective sources.
- In Stratified Data Sampling technique, one needs to be clear about the kind of strata or group it will be making.
- Inertia of Large Numbers: As mentioned in the first principle, this too states that the parent data set must be large enough to gain better and clear results.
Conclusion
Data sampling is a powerful tool for extracting insights from large datasets, enabling researchers to make informed decisions and draw accurate conclusions about populations. By understanding the principles, techniques, and best practices of data sampling, researchers can maximize the effectiveness and reliability of their analyses, ultimately leading to better outcomes in research and decision-making processes.